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K-Means clustering via the Frank-Wolfe algorithm

: Bauckhage, C.

Fulltext ()

Krestel, R.:
LWDA 2016, Lernen, Wissen, Daten, Analysen : Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" Potsdam, Germany, September 12-14, 2016
Potsdam, 2016 (CEUR Workshop Proceedings 1670)
ISSN: 1613-0073
Conference "Lernen, Wissen, Daten, Analysen" (LWDA) <2016, Potsdam>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()

We show that k-means clustering is a matrix factorization problem. Seen from this point of view, k-means clustering can be computed using alternating least squares techniques and we show how the constrained optimization steps involved in this procedure can be solved efficiently using the Frank-Wolfe algorithm.